A multiple-play bandit algorithm applied to recommender systems

Jonathan Louëdec (1, 2) , Max Chevalier (1, 2) , Josiane Mothe (1, 3) , Aurélien Garivier (4) , Sébastien Gerchinovitz (4)

1 IRIT-SIG - Systèmes d’Informations Généralisées
2 UT3 - Université Toulouse III - Paul Sabatier
3 UT2J - Université Toulouse - Jean Jaurès
4 IMT - Institut de Mathématiques de Toulouse UMR5219

For several web tasks such as ad placement or e-commerce, recommender systems must recommend multiple items to their users-such problems can be modeled as bandits with multiple plays. State-of-the-art methods require running as many single-play bandit algorithms as there are items to recommend. On the contrary, some recent theoretical work in the machine learning literature designed new algorithms to address the multiple-play case directly. These algorithms were proved to have strong theoretical guarantees. In this paper we compare one such multiple-play algorithm with previous methods. We show on two real-world datasets that the multiple-play algorithm we use converges to equivalent values but learns about three times faster than state-of-the-art methods. We also show that carefully adapting these earlier methods can improve their performance.

Mots clés: Information retrieval; Reinforcement learning; Recommender systems; Bandit problems

Domaines: Théorie de l'information [cs.IT]; Recherche d'information [cs.IR]

Jonathan Louëdec, Max Chevalier, Josiane Mothe, Aurélien Garivier, Sébastien Gerchinovitz. A multiple-play bandit algorithm applied to recommender systems. 28th International Florida Artificial Intelligence Research Society (FLAIRS 2015), May 2015, Hollywood, United States. pp.67-72. ⟨hal-04077707⟩